# adapted from https://github.com/openai/CLIP/blob/main/clip/model.py
from collections import OrderedDict
from typing import Tuple, Union
from math import sqrt

import numpy as np
import torch
import torch.nn.functional as F
from torch import nn


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1):
        super().__init__()

        # all conv layers have stride 1. an avgpool is performed after the second convolution when stride > 1
        self.conv1 = nn.Conv2d(inplanes, planes, 1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu1 = nn.ReLU(inplace=True)

        self.conv2 = nn.Conv2d(planes, planes, 3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.relu2 = nn.ReLU(inplace=True)

        self.avgpool = nn.AvgPool2d(stride) if stride > 1 else nn.Identity()

        self.conv3 = nn.Conv2d(planes, planes * self.expansion, 1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion)
        self.relu3 = nn.ReLU(inplace=True)

        self.downsample = None
        self.stride = stride

        if stride > 1 or inplanes != planes * Bottleneck.expansion:
            # downsampling layer is prepended with an avgpool, and the subsequent convolution has stride 1
            self.downsample = nn.Sequential(OrderedDict([
                ("-1", nn.AvgPool2d(stride)),
                ("0", nn.Conv2d(inplanes, planes * self.expansion, 1, stride=1, bias=False)),
                ("1", nn.BatchNorm2d(planes * self.expansion))
            ]))

    def forward(self, x: torch.Tensor):
        identity = x

        out = self.relu1(self.bn1(self.conv1(x)))
        out = self.relu2(self.bn2(self.conv2(out)))
        out = self.avgpool(out)
        out = self.bn3(self.conv3(out))

        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu3(out)
        return out


class AttentionPool2d(nn.Module):
    def __init__(self, spacial_dim: int, embed_dim: int, num_heads: int, output_dim: int = None):
        super().__init__()
        self.positional_embedding = nn.Parameter(torch.randn(spacial_dim ** 2 + 1, embed_dim) / embed_dim ** 0.5)
        self.k_proj = nn.Linear(embed_dim, embed_dim)
        self.q_proj = nn.Linear(embed_dim, embed_dim)
        self.v_proj = nn.Linear(embed_dim, embed_dim)
        self.c_proj = nn.Linear(embed_dim, output_dim or embed_dim)
        self.num_heads = num_heads

    # noel: adapted from https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
    @staticmethod
    def interpolate_positional_embedding(positional_embedding: torch.Tensor, size: Tuple[int, int]):
        # positional_embedding_patch: ((input_resolution // patch_size) ** 2) + 1) x n_dims
        positional_embedding_cls = positional_embedding[0, :]  # n_dims
        positional_embedding_patch = positional_embedding[1:, :]  # (h * w) x n_dims

        hw, n_dims = positional_embedding_patch.shape

        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        h, w = size[0] + 0.1, size[1] + 0.1
        patch_pos_embed = nn.functional.interpolate(
            positional_embedding_patch.view(1, int(sqrt(hw)), int(sqrt(hw)), n_dims).permute(0, 3, 1, 2),
            scale_factor=(h / sqrt(hw), w / sqrt(hw)),  # (h, w) format
            mode='bicubic',
        )  # 1 x n_dims x size[0] x size[1]
        assert int(h) == patch_pos_embed.shape[-2] and int(w) == patch_pos_embed.shape[-1]

        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, n_dims)
        return torch.cat((positional_embedding_cls[None, None], patch_pos_embed), dim=1)

    # noel:
    # (1) enable to return patch tokens if needed;
    # (2) interpolate positional embedding according to an input shape.
    def forward(self, x, return_patch_tokens: bool = True):
        h_feat, w_feat = x.shape[-2:]
        x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
        x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC

        positional_embedding = self.interpolate_positional_embedding(
            positional_embedding=self.positional_embedding.to(x.dtype),
            size=(h_feat, w_feat)
        ).permute(1, 0, 2)
        x = x + positional_embedding  # (HW+1)NC

        x, _ = F.multi_head_attention_forward(
            query=x if return_patch_tokens else x[:1], key=x, value=x,
            # query=x[1:] if return_patch_tokens else x[:1], key=x[1:], value=x[1:],
            embed_dim_to_check=x.shape[-1],
            num_heads=self.num_heads,
            q_proj_weight=self.q_proj.weight,
            k_proj_weight=self.k_proj.weight,
            v_proj_weight=self.v_proj.weight,
            in_proj_weight=None,
            in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
            bias_k=None,
            bias_v=None,
            add_zero_attn=False,
            dropout_p=0,
            out_proj_weight=self.c_proj.weight,
            out_proj_bias=self.c_proj.bias,
            use_separate_proj_weight=True,
            training=self.training,
            need_weights=False
        )
        return x.squeeze(0)

    # original implementation
    # def forward(self, x):
    #     x = x.flatten(start_dim=2).permute(2, 0, 1)  # NCHW -> (HW)NC
    #     x = torch.cat([x.mean(dim=0, keepdim=True), x], dim=0)  # (HW+1)NC
    #     x = x + self.positional_embedding[:, None, :].to(x.dtype)  # (HW+1)NC
    #     x, _ = F.multi_head_attention_forward(
    #         # query=x[:1], key=x, value=x,
    #         query=x, key=x, value=x,
    #         embed_dim_to_check=x.shape[-1],
    #         num_heads=self.num_heads,
    #         q_proj_weight=self.q_proj.weight,
    #         k_proj_weight=self.k_proj.weight,
    #         v_proj_weight=self.v_proj.weight,
    #         in_proj_weight=None,
    #         in_proj_bias=torch.cat([self.q_proj.bias, self.k_proj.bias, self.v_proj.bias]),
    #         bias_k=None,
    #         bias_v=None,
    #         add_zero_attn=False,
    #         dropout_p=0,
    #         out_proj_weight=self.c_proj.weight,
    #         out_proj_bias=self.c_proj.bias,
    #         use_separate_proj_weight=True,
    #         training=self.training,
    #         need_weights=False
    #     )
    #     return x.squeeze(0)


class ModifiedResNet(nn.Module):
    """
    A ResNet class that is similar to torchvision's but contains the following changes:
    - There are now 3 "stem" convolutions as opposed to 1, with an average pool instead of a max pool.
    - Performs anti-aliasing strided convolutions, where an avgpool is prepended to convolutions with stride > 1
    - The final pooling layer is a QKV attention instead of an average pool
    """

    def __init__(self, layers, output_dim, heads, input_resolution=224, width=64):
        super().__init__()
        self.output_dim = output_dim
        self.input_resolution = input_resolution

        # the 3-layer stem
        self.conv1 = nn.Conv2d(3, width // 2, kernel_size=3, stride=2, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(width // 2)
        self.relu1 = nn.ReLU(inplace=True)
        self.conv2 = nn.Conv2d(width // 2, width // 2, kernel_size=3, padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(width // 2)
        self.relu2 = nn.ReLU(inplace=True)
        self.conv3 = nn.Conv2d(width // 2, width, kernel_size=3, padding=1, bias=False)
        self.bn3 = nn.BatchNorm2d(width)
        self.relu3 = nn.ReLU(inplace=True)
        self.avgpool = nn.AvgPool2d(2)

        # residual layers
        self._inplanes = width  # this is a *mutable* variable used during construction
        self.layer1 = self._make_layer(width, layers[0])
        self.layer2 = self._make_layer(width * 2, layers[1], stride=2)
        self.layer3 = self._make_layer(width * 4, layers[2], stride=2)
        self.layer4 = self._make_layer(width * 8, layers[3], stride=2)

        embed_dim = width * 32  # the ResNet feature dimension
        self.attnpool = AttentionPool2d(input_resolution // 32, embed_dim, heads, output_dim)

        # # noel
        # self.attnpool.requires_grad_(False)

        self.n_vision_dims = self.width = embed_dim
        self.n_text_dims: int = 1024

    def init(self, frozen_bn: bool):
        # self.v_proj = nn.Conv2d(self.n_vision_dims, self.n_vision_dims, (1, 1))
        # self.v_proj.weight = nn.Parameter(self.attnpool.v_proj.weight[..., None, None], requires_grad=True)
        # self.v_proj.bias = nn.Parameter(self.attnpool.v_proj.bias, requires_grad=True)
        #
        # # self.c_proj = nn.Conv2d(self.n_vision_dims, self.n_text_dims, (1, 1))
        # # self.c_proj.weight = nn.Parameter(self.attnpool.c_proj.weight[..., None, None], requires_grad=True)
        # # self.c_proj.bias = nn.Parameter(self.attnpool.c_proj.bias, requires_grad=True)
        #
        # self.proj = nn.Conv2d(self.n_vision_dims, self.n_text_dims, (1, 1))
        # self.proj.weight = nn.Parameter(self.attnpool.c_proj.weight[..., None, None], requires_grad=True)
        # self.proj.bias = nn.Parameter(self.attnpool.c_proj.bias, requires_grad=True)

        self.proj = self.attnpool

        self.frozen_bn: bool = frozen_bn

    def _make_layer(self, planes, blocks, stride=1):
        layers = [Bottleneck(self._inplanes, planes, stride)]

        self._inplanes = planes * Bottleneck.expansion
        for _ in range(1, blocks):
            layers.append(Bottleneck(self._inplanes, planes))

        return nn.Sequential(*layers)

    # noel - enable to return patch tokens
    def forward(self, x, return_patch_tokens: bool = True):
        def stem(x):
            x = self.relu1(self.bn1(self.conv1(x)))
            x = self.relu2(self.bn2(self.conv2(x)))
            x = self.relu3(self.bn3(self.conv3(x)))
            x = self.avgpool(x)
            return x

        x = x.type(self.conv1.weight.dtype)
        x = stem(x)
        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        # x: b x n_dims x h_features x w_features
        h_feat, w_feat = x.shape[-2:]

        # # denseclip style
        # x = self.v_proj(x)
        # # x = self.c_proj(x)
        #
        # x = x.flatten(start_dim=-2).permute(0, 2, 1)  # b x (h_feat x w_feat) x n_dims
        # return x, h_feat, w_feat

        # x = self.attnpool(x, return_patch_tokens=return_patch_tokens)  # (1 + h_feat x w_feat) x b x n_dims
        #
        # # (1 + h_feat x w_feat) x b x n_dims -> b x (1 + h_feat x w_feat) x n_dims
        # x = x.permute(1, 0, 2)

        x = x.flatten(start_dim=-2).permute(0, 2, 1)  # b x (h_feat x w_feat) x n_dims
        return x, h_feat, w_feat

        if return_patch_tokens:
            # patch_tokens = x[:, 1:]
            # # patch_tokens = patch_tokens / (patch_tokens.norm(dim=-1, keepdim=True) + 1e-7)
            patch_tokens = x

            return patch_tokens, h_feat, w_feat
        else:
            return x[:, 0], h_feat, w_feat

    # original implementation
    # def forward(self, x):
    #     def stem(x):
    #         x = self.relu1(self.bn1(self.conv1(x)))
    #         x = self.relu2(self.bn2(self.conv2(x)))
    #         x = self.relu3(self.bn3(self.conv3(x)))
    #         x = self.avgpool(x)
    #         return x
    #
    #     x = x.type(self.conv1.weight.dtype)
    #     x = stem(x)
    #     x = self.layer1(x)
    #     x = self.layer2(x)
    #     x = self.layer3(x)
    #     x = self.layer4(x)
    #     x = self.attnpool(x)
    #
    #     return x


class LayerNorm(nn.LayerNorm):
    """Subclass torch's LayerNorm to handle fp16."""

    def forward(self, x: torch.Tensor):
        orig_type = x.dtype
        ret = super().forward(x.type(torch.float32))
        return ret.type(orig_type)


class QuickGELU(nn.Module):
    def forward(self, x: torch.Tensor):
        return x * torch.sigmoid(1.702 * x)


class ResidualAttentionBlock(nn.Module):
    def __init__(self, d_model: int, n_head: int, attn_mask: torch.Tensor = None):
        super().__init__()

        self.attn = nn.MultiheadAttention(d_model, n_head)
        self.ln_1 = LayerNorm(d_model)
        self.mlp = nn.Sequential(OrderedDict([
            ("c_fc", nn.Linear(d_model, d_model * 4)),
            ("gelu", QuickGELU()),
            ("c_proj", nn.Linear(d_model * 4, d_model))
        ]))
        self.ln_2 = LayerNorm(d_model)
        self.attn_mask = attn_mask

    def attention(self, x: torch.Tensor):
        self.attn_mask = self.attn_mask.to(dtype=x.dtype, device=x.device) if self.attn_mask is not None else None
        return self.attn(x, x, x, need_weights=False, attn_mask=self.attn_mask)[0]

    def forward(self, x: torch.Tensor):
        x = x + self.attention(self.ln_1(x))
        x = x + self.mlp(self.ln_2(x))
        return x


class Transformer(nn.Module):
    def __init__(self, width: int, layers: int, heads: int, attn_mask: torch.Tensor = None):
        super().__init__()
        self.width = width
        self.layers = layers
        self.resblocks = nn.Sequential(*[ResidualAttentionBlock(width, heads, attn_mask) for _ in range(layers)])

    def forward(self, x: torch.Tensor):
        return self.resblocks(x)


class VisionTransformer(nn.Module):
    def __init__(self, input_resolution: int, patch_size: int, width: int, layers: int, heads: int, output_dim: int):
        super().__init__()
        self.input_resolution = input_resolution
        self.output_dim = output_dim
        self.conv1 = nn.Conv2d(in_channels=3, out_channels=width, kernel_size=patch_size, stride=patch_size, bias=False)

        scale = width ** -0.5
        self.class_embedding = nn.Parameter(scale * torch.randn(width))  # n_dims
        self.positional_embedding = nn.Parameter(scale * torch.randn((input_resolution // patch_size) ** 2 + 1, width))
        self.ln_pre = LayerNorm(width)

        self.transformer = Transformer(width, layers, heads)

        self.ln_post = LayerNorm(width)
        self.proj = nn.Parameter(scale * torch.randn(width, output_dim))

        # noel
        self.width = width  # 768

    # noel: adapted from https://github.com/facebookresearch/dino/blob/main/vision_transformer.py
    @staticmethod
    def interpolate_positional_embedding(positional_embedding: torch.Tensor, size: Tuple[int, int]):
        # positional_embedding_patch: ((input_resolution // patch_size) ** 2) + 1) x n_dims
        positional_embedding_cls = positional_embedding[0, :]  # n_dims
        positional_embedding_patch = positional_embedding[1:, :]  # (h * w) x n_dims

        hw, n_dims = positional_embedding_patch.shape

        # we add a small number to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        h, w = size[0] + 0.1, size[1] + 0.1
        patch_pos_embed = nn.functional.interpolate(
            positional_embedding_patch.view(1, int(sqrt(hw)), int(sqrt(hw)), n_dims).permute(0, 3, 1, 2),
            scale_factor=(h / sqrt(hw), w / sqrt(hw)),  # (h, w) format
            mode='bicubic',
        )
        assert int(h) == patch_pos_embed.shape[-2] and int(w) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, n_dims)
        return torch.cat((positional_embedding_cls[None, None], patch_pos_embed), dim=1)

    # noel
    def forward(self, x: torch.Tensor):
        patch_tokens = self.conv1(x)  # shape = [*, n_dims, h, w]
        b, c, h_feat, w_feat = patch_tokens.shape

        patch_tokens = patch_tokens.reshape(patch_tokens.shape[0], patch_tokens.shape[1], -1)  # shape = [*, n_dims, h * w]
        patch_tokens = patch_tokens.permute(0, 2, 1)  # shape = [*, h * w, n_dims]

        tokens = torch.cat(
            [
                self.class_embedding[None, None].to(x.dtype) + torch.zeros(b, 1, c, dtype=patch_tokens.dtype, device=x.device),
                patch_tokens  # b x h * w x n_dims
            ],
            dim=1
        )  # b x (1 + h * w) x n_dims

        positional_embedding = self.interpolate_positional_embedding(
            positional_embedding=self.positional_embedding, size=(h_feat, w_feat)
        )  # 1 x (1 + h * w) x n_dims

        tokens = tokens + positional_embedding.to(x.dtype)  # b x (1 + h * w) x n_dims
        tokens = self.ln_pre(tokens)

        tokens = tokens.permute(1, 0, 2)  # b * (1 + h * w) x n_dims -> (1 + h * w) x b x n_dims
        tokens = self.transformer(tokens)
        tokens = tokens.permute(1, 0, 2)  # (1 + h * w) x b x n_dims -> b * (1 + h * w) x n_dims

        patch_tokens = tokens[:, 1:, :]  # b x (h * w) x n_dims
        patch_tokens = self.ln_post(patch_tokens)  # normalise over the last dimension

        # patch_tokens = F.layer_norm(patch_tokens, normalized_shape=patch_tokens.shape[-2:])

        # self.proj: width x output_dim
        # patch_tokens: b x (h * w) x n_dims -> b x (h * w) x text_dim
        # patch_tokens = patch_tokens @ self.proj
        return patch_tokens, h_feat, w_feat

    # original
    # def forward(self, x: torch.Tensor):
    #     x = self.conv1(x)  # shape = [*, width, grid, grid]
    #     x = x.reshape(x.shape[0], x.shape[1], -1)  # shape = [*, width, grid ** 2]
    #     x = x.permute(0, 2, 1)  # shape = [*, grid ** 2, width]
    #     x = torch.cat([self.class_embedding.to(x.dtype) + torch.zeros(x.shape[0], 1, x.shape[-1], dtype=x.dtype, device=x.device), x], dim=1)  # shape = [*, grid ** 2 + 1, width]
    #     x = x + self.positional_embedding.to(x.dtype)
    #     x = self.ln_pre(x)
    #
    #     x = x.permute(1, 0, 2)  # NLD -> LND
    #     x = self.transformer(x)
    #     x = x.permute(1, 0, 2)  # LND -> NLD
    #
    #     x = self.ln_post(x[:, 0, :])
    #
    #     if self.proj is not None:
    #         x = x @ self.proj
    #
    #     return x


class CLIP(nn.Module):
    def __init__(self,
                 embed_dim: int,
                 # vision
                 image_resolution: int,
                 vision_layers: Union[Tuple[int, int, int, int], int],
                 vision_width: int,
                 vision_patch_size: int,
                 # text
                 context_length: int,
                 vocab_size: int,
                 transformer_width: int,
                 transformer_heads: int,
                 transformer_layers: int
                 ):
        super().__init__()

        self.context_length = context_length

        if isinstance(vision_layers, (tuple, list)):
            vision_heads = vision_width * 32 // 64
            self.visual = ModifiedResNet(
                layers=vision_layers,
                output_dim=embed_dim,
                heads=vision_heads,
                input_resolution=image_resolution,
                width=vision_width
            )
        else:
            vision_heads = vision_width // 64
            self.visual = VisionTransformer(
                input_resolution=image_resolution,
                patch_size=vision_patch_size,
                width=vision_width,
                layers=vision_layers,
                heads=vision_heads,
                output_dim=embed_dim
            )

        self.transformer = Transformer(
            width=transformer_width,
            layers=transformer_layers,
            heads=transformer_heads,
            attn_mask=self.build_attention_mask()
        )

        self.vocab_size = vocab_size
        self.token_embedding = nn.Embedding(vocab_size, transformer_width)
        self.positional_embedding = nn.Parameter(torch.empty(self.context_length, transformer_width))
        self.ln_final = LayerNorm(transformer_width)

        self.text_projection = nn.Parameter(torch.empty(transformer_width, embed_dim))
        self.logit_scale = nn.Parameter(torch.ones([]) * np.log(1 / 0.07))

        self.initialize_parameters()

    def initialize_parameters(self):
        nn.init.normal_(self.token_embedding.weight, std=0.02)
        nn.init.normal_(self.positional_embedding, std=0.01)

        if isinstance(self.visual, ModifiedResNet):
            if self.visual.attnpool is not None:
                std = self.visual.attnpool.c_proj.in_features ** -0.5
                nn.init.normal_(self.visual.attnpool.q_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.k_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.v_proj.weight, std=std)
                nn.init.normal_(self.visual.attnpool.c_proj.weight, std=std)

            for resnet_block in [self.visual.layer1, self.visual.layer2, self.visual.layer3, self.visual.layer4]:
                for name, param in resnet_block.named_parameters():
                    if name.endswith("bn3.weight"):
                        nn.init.zeros_(param)

        proj_std = (self.transformer.width ** -0.5) * ((2 * self.transformer.layers) ** -0.5)
        attn_std = self.transformer.width ** -0.5
        fc_std = (2 * self.transformer.width) ** -0.5
        for block in self.transformer.resblocks:
            nn.init.normal_(block.attn.in_proj_weight, std=attn_std)
            nn.init.normal_(block.attn.out_proj.weight, std=proj_std)
            nn.init.normal_(block.mlp.c_fc.weight, std=fc_std)
            nn.init.normal_(block.mlp.c_proj.weight, std=proj_std)

        if self.text_projection is not None:
            nn.init.normal_(self.text_projection, std=self.transformer.width ** -0.5)

    def build_attention_mask(self):
        # lazily create causal attention mask, with full attention between the vision tokens
        # pytorch uses additive attention mask; fill with -inf
        mask = torch.empty(self.context_length, self.context_length)
        mask.fill_(float("-inf"))
        mask.triu_(1)  # zero out the lower diagonal
        return mask

    @property
    def dtype(self):
        return self.visual.conv1.weight.dtype

    def encode_image(self, image):
        return self.visual(image.type(self.dtype))

    def encode_text(self, text):
        x = self.token_embedding(text).type(self.dtype)  # [batch_size, n_ctx, d_model]

        x = x + self.positional_embedding.type(self.dtype)
        x = x.permute(1, 0, 2)  # NLD -> LND
        x = self.transformer(x)
        x = x.permute(1, 0, 2)  # LND -> NLD
        x = self.ln_final(x).type(self.dtype)

        # x.shape = [batch_size, n_ctx, transformer.width]
        # take features from the eot embedding (eot_token is the highest number in each sequence)
        x = x[torch.arange(x.shape[0]), text.argmax(dim=-1)] @ self.text_projection

        return x

    def forward(self, image, text):
        image_features = self.encode_image(image)
        text_features = self.encode_text(text)

        # normalized features
        image_features = image_features / image_features.norm(dim=1, keepdim=True)
        text_features = text_features / text_features.norm(dim=1, keepdim=True)

        # cosine similarity as logits
        logit_scale = self.logit_scale.exp()
        logits_per_image = logit_scale * image_features @ text_features.t()
        logits_per_text = logits_per_image.t()

        # shape = [global_batch_size, global_batch_size]
        return logits_per_image, logits_per_text


def convert_weights(model: nn.Module):
    """Convert applicable model parameters to fp16"""

    def _convert_weights_to_fp16(l):
        if isinstance(l, (nn.Conv1d, nn.Conv2d, nn.Linear)):
            l.weight.data = l.weight.data.half()
            if l.bias is not None:
                l.bias.data = l.bias.data.half()

        if isinstance(l, nn.MultiheadAttention):
            for attr in [*[f"{s}_proj_weight" for s in ["in", "q", "k", "v"]], "in_proj_bias", "bias_k", "bias_v"]:
                tensor = getattr(l, attr)
                if tensor is not None:
                    tensor.data = tensor.data.half()

        for name in ["text_projection", "proj"]:
            if hasattr(l, name):
                attr = getattr(l, name)
                if attr is not None:
                    attr.data = attr.data.half()

    model.apply(_convert_weights_to_fp16)


def build_model(state_dict: dict):
    vit = "visual.proj" in state_dict

    if vit:
        vision_width = state_dict["visual.conv1.weight"].shape[0]
        vision_layers = len([k for k in state_dict.keys() if k.startswith("visual.") and k.endswith(".attn.in_proj_weight")])
        vision_patch_size = state_dict["visual.conv1.weight"].shape[-1]
        grid_size = round((state_dict["visual.positional_embedding"].shape[0] - 1) ** 0.5)
        image_resolution = vision_patch_size * grid_size
    else:
        counts: list = [len(set(k.split(".")[2] for k in state_dict if k.startswith(f"visual.layer{b}"))) for b in [1, 2, 3, 4]]
        vision_layers = tuple(counts)
        vision_width = state_dict["visual.layer1.0.conv1.weight"].shape[0]
        output_width = round((state_dict["visual.attnpool.positional_embedding"].shape[0] - 1) ** 0.5)
        vision_patch_size = None
        assert output_width ** 2 + 1 == state_dict["visual.attnpool.positional_embedding"].shape[0]
        image_resolution = output_width * 32

    embed_dim = state_dict["text_projection"].shape[1]
    context_length = state_dict["positional_embedding"].shape[0]
    vocab_size = state_dict["token_embedding.weight"].shape[0]
    transformer_width = state_dict["ln_final.weight"].shape[0]
    transformer_heads = transformer_width // 64
    transformer_layers = len(set(k.split(".")[2] for k in state_dict if k.startswith("transformer.resblocks")))

    model = CLIP(
        embed_dim,
        image_resolution, vision_layers, vision_width, vision_patch_size,
        context_length, vocab_size, transformer_width, transformer_heads, transformer_layers
    )

    for key in ["input_resolution", "context_length", "vocab_size"]:
        if key in state_dict:
            del state_dict[key]

    convert_weights(model)
    model.load_state_dict(state_dict)
    return model.eval()
